Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images

作     者：Puvanathasan, Prabakar Bizheva, Kostadinka 

作者机构：Univ Waterloo Dept Phys & Astron Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada 

出 版 物：《OPTICS EXPRESS》 (Opt. Express)

年 卷 期：2009年第17卷第2期

页      面：733-746页

核心收录：

学科分类：070207[理学-光学] 07[理学] 08[工学] 0803[工学-光学工程] 0702[理学-物理学] 

基　　金：NSERC University of Waterloo 

主　　题：.diffusion coefficient Filtering speckle noise algorithm based fuzzy sets anisotropic diffusion coherence tomography Edginess Adaptive Lee 

摘      要：A novel, speckle noise reduction algorithm based on the combination of Anisotropic Diffusion (AD) filtering and Interval Type-II fuzzy sets was developed for reducing speckle noise in Optical Coherence Tomography (OCT) images. Unlike regular AD, the new Type-II fuzzy AD algorithm considers the uncertainty in the calculated diffusion coefficient and appropriate adjustments to the coefficient are made. The new algorithm offers flexibility in optimizing the trade-off between two of the image metrics: signal-to-noise (SNR) and Edginess, which are directly related to the structure of the imaged object. Application of the Type-II fuzzy AD algorithm to OCT tomograms acquired in-vivo from a human finger tip and human retina show reduction in the speckle noise with very little edge blurring and about 13 dB and 7 dB image SNR improvement respectively. Comparison with Wiener, Adaptive Lee and regular AD filters, applied to the same images, demonstrates the superior performance of the Type-II fuzzy AD algorithm in terms image SNR and edge preservation metrics improvement. (C) 2008 Optical Society of America